Learning error models for graph SLAM
Résumé
Following recent developments, this paper investigates the possibility to predict uncertainty models for monocu-lar graph SLAM using topological features of the problem. An architecture to learn relative (i.e. inter-keyframe) uncertainty models using the resistance distance in the covisibility graph is presented. The proposed architecture is applied to simulated UAV coverage path planning trajectories and an analysis of the approaches strengths and shortcomings is provided.
Domaines
Robotique [cs.RO]Origine | Fichiers produits par l'(les) auteur(s) |
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